Visualizzare il rendimento di approvazione dei prestiti

Nel settore dei servizi finanziari, capire i fattori che influenzano i tassi di approvazione dei prestiti è fondamentale sia per i finanziatori sia per i richiedenti. Un istituto finanziario ha condotto uno studio e raccolto dati sulle richieste di prestito, includendo l’importo richiesto, il punteggio di credito del richiedente, lo stato occupazionale e il rendimento finale del processo di approvazione. Questo ricco insieme di dati offre uno sguardo sulle dinamiche sottili che intervengono nelle decisioni sui prestiti. Ti è stato chiesto di esplorare il dataset loan_approval_yield per capire come gli importi dei prestiti e i punteggi di credito influenzino i rendimenti di approvazione.

Il DataFrame loan_approval_yield, seaborn come sns e matplotlib.pyplot come plt sono già stati caricati per te.

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Progettazione Sperimentale in Python

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# Use Seaborn to create the bar graph
sns.catplot(x="____", 
            y="____", 
            hue="____", 
            kind="____", 
            data=loan_approval_yield)
plt.title("Loan Approval Yield by Amount and Credit Score")
plt.show()

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Questo esercizio fa parte del corso

Progettazione Sperimentale in Python

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Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.

Exercise 1: Setting up experiments Exercise 2: Non-random assignment of subjects Exercise 3: Random assignment of subjects Exercise 4: Experimental data setup Exercise 5: Blocking experimental data Exercise 6: Stratifying an experiment Exercise 7: Which was stratified?Exercise 8: Normal data Exercise 9: Visual normality in an agricultural experiment Exercise 10: Analytical normality in an agricultural experiment

You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.

Exercise 1: Factorial designs: principles and applications Exercise 2: Understanding marketing campaign effectiveness Exercise 3: Heatmap of campaign interactions Exercise 4: Factorial designs and randomized block designs Exercise 5: Randomized block design: controlling variance Exercise 6: Implementing a randomized block design Exercise 7: Visualizing productivity within blocks by incentive Exercise 8: ANOVA within blocks of employees Exercise 9: Covariate adjustment in experimental design Exercise 10: Importance of covariates Exercise 11: Covariate adjustment with chick growth

Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.

Exercise 1: Choosing the right statistical test Exercise 2: Choosing the right test: petrochemicals Exercise 3: Choosing the right test: human resources Exercise 4: Choosing the right test: finance Exercise 5: Post-hoc analysis following ANOVA Exercise 6: Anxiety treatments ANOVA Exercise 7: Applying Tukey's HSD Exercise 8: Applying Bonferoni correction Exercise 9: P-values, alpha, and errors Exercise 10: Analyzing toy durability Exercise 11: Visualizing durability differences Exercise 12: Role of significance levels Exercise 13: Power analysis: sample and effect size Exercise 14: Effect size purpose Exercise 15: Estimating required sample size for energy study

Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.

Exercise 1: Sintetizzare insight da esperimenti complessi Exercise 2: Visualizzare il rendimento di approvazione dei prestiti

Esercizio in corso

Exercise 3: Esplorare la soddisfazione dei clienti Exercise 4: Comunicare in modo efficace i dati sperimentali Exercise 5: Affrontare le complessità nei dati sperimentali Exercise 6: Verifica l'eteroschedasticità nella shelf life Exercise 7: Esplorare e trasformare i dati sulla shelf life Exercise 8: Applicare test non parametrici nell'analisi sperimentale Exercise 9: Visualizzare e testare i metodi di conservazione Exercise 10: Analisi approfondita delle tecniche di conservazione degli alimenti Exercise 11: Congratulazioni!